Development and Properties of Sulfate-resistant and Corrosion-inhibiting Admixtures

Sulfate and chloride-induced corrosion of concrete and steel reinforcement are the most important causes of premature failure of durability of concrete structures. To prevent damage in concrete structures, the application of sulfate-resistant and corrosion-inhibiting admixtures has proven to be an effective method. In this study, a new type of corrosion-inhibiting admixtures including organic and special inorganic components are developed and the properties of mortar mixed with them was investigated. The results show that NC-CZ series of sulfate-resistant and corrosion-inhibiting admixtures have been successfully developed. The mortar with NC-CZ has good resistance to sulfate attack, whose corrosion resistance coefficient of mortar is 1.07, meeting the standard requirement and even larger than that of moderate sulfate-resistant Portland cement. The diffusion coefficient of chloride ion at 28d decreases by 35% around. Meanwhile, the water absorption is obviously decreased. The steel bars in mortar mixed with corrosion-inhibiting admixtures don’t occur rusting. By contrast, the steel bars in mortar without corrosion-inhibiting admixtures occur rusting, whose area rate of corrosion is more than 20%. This study could lead to significant benefits for durability and service life of reinforced concrete structures in China

Study on Performance of Negative Temperature and High Strength Bed Mortar Material for Wind Power Engineering

As the development process of affordable wind power projects accelerates, the height of tower hub shows a trend of development to 150m above. The technology of steel and concrete is widely applied. Bed mortar material, as the bonding material between precast concrete rings, is the key material to ensure the lifting speed of steel and concrete tower for wind power. In this study, the basic formula of negative temperature and high strength bed mortar material was explored, and its working performance and strength development under different curing conditions were further studied. The results show that the developed bed mortar material has excellent thixotropy and it is still operable at 50min. Under the condition of negative temperature curing, the early strength of bed mortar material is high, and the late strength develops well. Curing at ultra-low temperature of -15℃, the strength of -1d is 35.4MPa, and the strength of -7+21d is over 90MPa. In the outdoor natural curing environment of alternating positive and negative temperatures, the strength of 1d reaches 51.1MPa, the strength of 60d is 113.2MPa. The performance of bed mortar material far meets the requirements of the strength grade of 80MPa which is used in winter construction of wind power engineering

Full-Memory Transformer for Image Captioning

The Transformer-based approach represents the state-of-the-art in image captioning. However, existing studies have shown Transformer has a problem that irrelevant tokens with overlapping neighbors incorrectly attend to each other with relatively large attention scores. We believe that this limitation is due to the incompleteness of the Self-Attention Network (SAN) and Feed-Forward Network (FFN). To solve this problem, we present the Full-Memory Transformer method for image captioning. The method improves the performance of both image encoding and language decoding. In the image encoding step, we propose the Full-LN symmetric structure, which enables stable training and better model generalization performance by symmetrically embedding Layer Normalization on both sides of the SAN and FFN. In the language decoding step, we propose the Memory Attention Network (MAN), which extends the traditional attention mechanism to determine the correlation between attention results and input sequences, guiding the model to focus on the words that need to be attended to. Our method is evaluated on the MS COCO dataset and achieves good performance, improving the result in terms of BLEU-4 from 38.4 to 39.3

RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark

Power line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. However, in the power line inspection based on computer vision, datasets have a significant impact on deep learning. The lack of public high-quality power scene data hinders the application of deep learning. To address this problem, we built a dataset for power line inspection scenes, named RSIn-Dataset. RSIn-Dataset contains 4 categories and 1887 images, with abundant backgrounds. Then, we used mainstream object detection methods to build a benchmark, providing reference for insulator detection. In addition, to address the problem of detection inefficiency caused by large model parameters, an improved YoloV4 is proposed, named YoloV4++. It uses a lightweight network, i.e., MobileNetv1, as the backbone, and employs the depthwise separable convolution to replace the standard convolution. Meanwhile, the focal loss is implemented in the loss function to solve the impact of sample imbalance. The experimental results show the effectiveness of YoloV4++. The mAP and FPS can reach 94.24% and 53.82 FPS, respectively

Multi-Tier 3D Trajectory Planning for Cellular-Connected UAVs in Complex Urban Environments

Cellular-connected unmanned aerial vehicles (UAVs) present a viable solution to address communication and navigation limitations by leveraging base stations for air–ground communication. However, in complex urban scenarios with stringent communication requirements, achieving asymmetrical control becomes crucial to strike a balance between communication reliability and flight safety. Moreover, existing cellular-connected UAV trajectory planning algorithms often struggle to handle real scenes with sudden and intricate obstacles. To address the aforementioned challenges, this paper presents the multi-tier trajectory planning method (MTTP), which takes into account air–ground communication service assurance and collision avoidance in intricate urban environments. The proposed approach establishes a flight risk model that accounts for both the outage probability of UAV-ground base station (GBS) communication and the complexity of flight environments, and transforms the inherently complex three-dimensional (3D) trajectory optimization problem into a risk distance minimization model. To optimize the flight trajectory, a hierarchical progressive solution approach is proposed, which combines the strengths of the heuristic search algorithm (HSA) and deep reinforcement learning (DRL) algorithm. This innovative fusion of techniques empowers MTTP to efficiently navigate complex scenarios with sudden obstacles and communication challenges. Simulations show that the proposed MTTP method achieves a more superior performance of trajectory planning than the conventional communication-based solution, which yields a substantial reduction in flight distance of at least 8.49% and an impressive 10% increase in the mission success rate. Furthermore, a real-world scenario is chosen from the Yuhang District, Hangzhou (a southern Chinese city), to validate the practical applicability of the MTTP method in highly complex operating scenarios

Relating Doses of Contrast Agent Administered to TIC and Semi-Quantitative Parameters on DCE-MRI: Based on a Murine Breast Tumor Model.

OBJECTIVE:To explore the changes in the time-signal intensity curve(TIC) type and semi-quantitative parameters of dynamic contrast-enhanced(DCE)imaging in relation to variations in the contrast agent(CA) dosage in the Walker 256 murine breast tumor model, and to determine the appropriate parameters for the evaluation ofneoadjuvantchemotherapy(NAC)response. MATERIALS AND METHODS:Walker 256 breast tumor models were established in 21 rats, which were randomly divided into three groups of7rats each. Routine scanning and DCE-magnetic resonance imaging (MRI) of the rats were performed using a 7T MR scanner. The three groups of rats were administered different dosages of the CA0.2mmol/kg, 0.3mmol/kg, and 0.5mmol/kg, respectively; and the corresponding TICs the semi-quantitative parameters were calculated and compared among the three groups. RESULTS:The TICs were not influenced by the CA dosage and presented a washout pattern in all of the tumors evaluated and weren't influenced by the CA dose. The values of the initial enhancement percentage(Efirst), initial enhancement velocity(Vfirst), maximum signal(Smax), maximum enhancement percentage(Emax), washout percentage(Ewash), and signal enhancement ratio(SER) showed statistically significant differences among the three groups (F = 16.952, p = 0.001; F = 69.483, p<0.001; F = 54.838, p<0.001; F = 12.510, p = 0.003; F = 5.248, p = 0.031; F = 9.733, p = 0.006, respectively). However, the values of the time to peak(Tpeak), maximum enhancement velocity(Vmax), and washout velocity(Vwash)did not differ significantly among the three dosage groups (F = 0.065, p = 0.937; F = 1.505, p = 0.273; χ2 = 1.423, p = 0.319, respectively); the washout slope(Slopewash), too, was uninfluenced by the dosage(F = 1.654, p = 0.244). CONCLUSION:The CA dosage didn't affect the TIC type, Tpeak, Vmax, Vwash or Slopewash. These dose-independent parameters as well as the TIC type might be more useful for monitoring the NAC response because they allow the comparisons of the DCE data obtained using different CA dosages

Residue detection and correlation analysis of multiple neonicotinoid insecticides and their metabolites in edible herbs

In this work, a green analytical method was established for the simultaneous extraction and detection of 20 analytes–10 neonicotinoid insecticides and their 10 major toxic metabolites in edible herbs. QuEChERS and LC-MS/MS were used to analyze the 20 analytes in five edible herbs. The residues of the 20 neonicotinoid insecticides and their metabolites in 109 herbal samples were detected, of which 90 samples were positive, and the residue of total neonicotinoid insecticides ranged from 0.26 to 139.28 μg/kg. Acetamiprid (77.06 %, ≤85.95 μg/kg), imidacloprid (67.89 %, ≤32.49 μg/kg) and their metabolites (N-desmethyl-acetamiprid (44.04 %, ≤18.42 μg/kg) and desnitro imidacloprid (48.62 %, ≤16.55 μg/kg) were most frequently detected in herbs. Significant positive correlations were found between imidacloprid/acetamiprid and their metabolites in Lycii fructus and Citri reticulatae pericarpium. Therefore, more attention may be given to the neonicotinoid insecticide residues in edible herbs in the future

Research on Flexible Virtual Inertia Control Method Based on the Small Signal Model of DC Microgrid

Renewable energy is usually connected to the DC micro-grid by a large number of power electronic devices, which have the advantages of a fast system response, but the disadvantage to reduce the inertia of the system, which makes the stability of the system worse. It is necessary to increase the inertia of DC micro-grid so that it can recover and stabilize well when it receives a disturbance. In this paper, a small-signal model of DC micro-grid with constant power load (CPL) is established, and a flexible virtual inertial (FVI) control method based on DC bus voltage real-time variation is proposed, by controlling the DC/DC converter of the energy storage system, the problem of system oscillation caused by introducing voltage differential link to the system is solved. Compared with the droop control method, the FVI control method can increase the inertia of DC micro-grid system, reduce the influence of small disturbances, and improve the stability of the system. Finally, the validity of the FVI control method based on small signal model is verified in dSPACE